WEKO3
アイテム
Heterogeneous Sensor Fusion with GMPHD for Environmentally Adaptable Obstacle Detection in Mobility Systems
https://ipsj.ixsq.nii.ac.jp/records/211232
https://ipsj.ixsq.nii.ac.jp/records/2112327179445d-4a44-4a2d-acdc-527e0879a056
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IPSJ-TCDS1102002.pdf (2.7 MB)
|
Copyright (c) 2021 by the Information Processing Society of Japan
|
|
| オープンアクセス | ||
| Item type | Trans(1) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2021-05-18 | |||||||||
| タイトル | ||||||||||
| タイトル | Heterogeneous Sensor Fusion with GMPHD for Environmentally Adaptable Obstacle Detection in Mobility Systems | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Heterogeneous Sensor Fusion with GMPHD for Environmentally Adaptable Obstacle Detection in Mobility Systems | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | [コンシューマ・システム論文] obstacle detection, mobility systems, GMPHD, heterogeneous sensor fusion, T2TF, M2TF, T2AF | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||
| 資源タイプ | journal article | |||||||||
| 著者所属 | ||||||||||
| Center for Technology Innovation - Systems Engineering, Hitachi Ltd. Research and Development Group | ||||||||||
| 著者所属 | ||||||||||
| Center for Technology Innovation - Systems Engineering, Hitachi Ltd. Research and Development Group | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Center for Technology Innovation - Systems Engineering, Hitachi Ltd. Research and Development Group | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Center for Technology Innovation - Systems Engineering, Hitachi Ltd. Research and Development Group | ||||||||||
| 著者名 |
Chow, Man Yiu
× Chow, Man Yiu
× Mitsuhiro, Kitani
|
|||||||||
| 著者名(英) |
Chow, Man Yiu
× Chow, Man Yiu
× Mitsuhiro, Kitani
|
|||||||||
| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Obstacle detection is an essential process in consumer's autonomous mobility systems such as autonomous vehicles inside the dedicated lane to acquire the location of obstacles, and it has become a popular topic in this decade with the blooming of various object detection algorithms and the enhancement of sensor quality. To maintain high accuracy of obstacles' detection in mobility systems outdoor, a sensor fusion system is required to essentially support environmental influence such as lousy weather as well as high moving speeds and adaptably deal with clutter and miss detection based on the incoming measurements from heterogenous sensors with Camera, LiDAR and Radar. Since no current literature about Gaussian mixture probability hypothesis density (GMPHD) handles the above low accuracy fusion problem due to environmental influence for heterogeneous sensors, we propose the concept of integrating GMPHD to heterogeneous sensor fusion with three architectures, Track-to-Track-Fusion (T2TF), Measurement-to-Track-Fusion (M2TF) and Track-to-Association-Fusion (T2AF) and further evaluate their performances respectively in terms of their fusion improvement abilities to determine their practicalities for mobility systems by using the simulation datasets which reproduce ordinary and poorer conditions with the degradation of sensors' performance in the assumption of environmental influences. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.29(2021) (online) ------------------------------ |
|||||||||
| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Obstacle detection is an essential process in consumer's autonomous mobility systems such as autonomous vehicles inside the dedicated lane to acquire the location of obstacles, and it has become a popular topic in this decade with the blooming of various object detection algorithms and the enhancement of sensor quality. To maintain high accuracy of obstacles' detection in mobility systems outdoor, a sensor fusion system is required to essentially support environmental influence such as lousy weather as well as high moving speeds and adaptably deal with clutter and miss detection based on the incoming measurements from heterogenous sensors with Camera, LiDAR and Radar. Since no current literature about Gaussian mixture probability hypothesis density (GMPHD) handles the above low accuracy fusion problem due to environmental influence for heterogeneous sensors, we propose the concept of integrating GMPHD to heterogeneous sensor fusion with three architectures, Track-to-Track-Fusion (T2TF), Measurement-to-Track-Fusion (M2TF) and Track-to-Association-Fusion (T2AF) and further evaluate their performances respectively in terms of their fusion improvement abilities to determine their practicalities for mobility systems by using the simulation datasets which reproduce ordinary and poorer conditions with the degradation of sensors' performance in the assumption of environmental influences. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.29(2021) (online) ------------------------------ |
|||||||||
| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AA12628043 | |||||||||
| 書誌情報 |
情報処理学会論文誌コンシューマ・デバイス＆システム（CDS） 巻 11, 号 2, 発行日 2021-05-18 |
|||||||||
| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 2186-5728 | |||||||||
| 出版者 | ||||||||||
| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
